Article
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ExcessMortality Data and the Effect of the COVID-19 Vaccines Part 1: European Data
Version 1
: Received: 8 September 2023 / Approved: 11 September 2023 / Online: 11 September 2023 (11:31:34 CEST)
Version 2 : Received: 13 September 2023 / Approved: 14 September 2023 / Online: 15 September 2023 (05:26:17 CEST)
Version 2 : Received: 13 September 2023 / Approved: 14 September 2023 / Online: 15 September 2023 (05:26:17 CEST)
How to cite: Hegarty, P. ExcessMortality Data and the Effect of the COVID-19 Vaccines Part 1: European Data. Preprints 2023, 2023090674. https://doi.org/10.20944/preprints202309.0674.v1 Hegarty, P. ExcessMortality Data and the Effect of the COVID-19 Vaccines Part 1: European Data. Preprints 2023, 2023090674. https://doi.org/10.20944/preprints202309.0674.v1
Abstract
Using publicly available data for 28 EU/EES countries from Eurostat and Our World in Data, we investigate how the current rate of Covid vaccination in a country compares to its average rate of excess mortality (EM) in the pandemic to date. We find that, in the linear regression, the correlation between average EM and vaccination rate is strongly negative, a priori evidence to support the claim that the Covid vaccines have saved many lives. However, a closer analysis of the timeline suggests otherwise. The correlation was already strongly negative before the vaccines were rolled out and is only weakly negative thereafter. In theory, survivor bias could still explain this shift, especially since waves of EM closely align with Covid waves. However, we find in addition that about half of our 28 countries experienced higher EM in 2022 than in 2021, and all that did so have higher than average vaccination rates. This is something which survivor bias cannot explain and raises the real possibility that the vaccines have not just failed to save many lives, but may have already caused net harm. Moreover, any such harm may be ongoing since we find that EM and vaccination rates have been consistently positively correlated since April 2022. We show that all these findings are robust to several different ways of measuring EM and/or vaccination rates. Finally, using public data from Worldometers, we show that the correlation over time of official Covid mortality rates with current vaccination rates closely tracks that of EM rates, even as Covid mortality has waxed and waned and even in the post-omicron period.
Keywords
COVID-19; vaccination; all-cause mortality; excess mortality
Subject
Medicine and Pharmacology, Immunology and Allergy
Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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For simple linear regression, the correlation coefficient can be calculated from the regression parameters using the following formula:
$$correlation coefficient = sign(slope) \times \sqrt{\frac{SS(regression)}{SS(Total)}$$ (you can see the formula more clearly in a TeX or LaTex editor)
where:
- SS(regression) is the sum of squares due to regression,
- SS(Total) is the total sum of squares, which equals SS(regression) + SS(Error),
- sign(slope) is 1 if slope > 0, -1 if slope < 0, and 0 if slope = 0.
The graph for correlation usually looks like this
where in your case X can be vaccination rate and Y can be excess mortality rate.
I put the image in the comment with the icon "Insert image"